Semiparametric modeling of the spatial distribution of occupational accident risk in the casual labor market, Piracicaba, southeast Brazil

The objective of this study was to estimate the spatial distribution of work accident risk in the informal work market in the urban zone of an industrialized city in southeast Brazil and to examine concomitant effects of age, gender, and type of occupation after controlling for spatial risk variation. The basic methodology adopted was that of a population-based case-control study with particular interest focused on the spatial location of work. Cases were all casual workers in the city suffering work accidents during a one-year period; controls were selected from the source population of casual laborers by systematic random sampling of urban homes. The spatial distribution of work accidents was estimated via a semiparametric generalized additive model with a nonparametric bidimensional spline of the geographical coordinates of cases and controls as the nonlinear spatial component, and including age, gender, and occupation as linear predictive variables in the parametric component. We analyzed 1,918 cases and 2,245 controls between 1/11/2003 and 31/10/2004 in Piracicaba, Brazil. Areas of significantly high and low accident risk were identified in relation to mean risk in the study region (p < 0.01). Work accident risk for informal workers varied significantly in the study area. Significant age, gender, and occupational group effects on accident risk were identified after correcting for this spatial variation. A good understanding of high-risk groups and high-risk regions underpins the formulation of hypotheses concerning accident causality and the development of effective public accident prevention policies.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Stochastic methods based on time-series modeling combined with geostatistics can be useful tools to describe the variability of water-table levels in time and space and to account for uncertainty. Monitoring water-level networks can give information about the dynamic of the aquifer domain in both dimensions. Time-series modeling is an elegant way to treat monitoring data without the complexity of physical mechanistic models. Time-series model predictions can be interpolated spatially, with the spatial differences in water-table dynamics determined by the spatial variation in the system properties and the temporal variation driven by the dynamics of the inputs into the system. An integration of stochastic methods is presented, based on time-series modeling and geostatistics as a framework to predict water levels for decision making in groundwater management and land-use planning. The methodology is applied in a case study in a Guarani Aquifer System (GAS) outcrop area located in the southeastern part of Brazil. Communication of results in a clear and understandable form, via simulated scenarios, is discussed as an alternative, when translating scientific knowledge into applications of stochastic hydrogeology in large aquifers with limited monitoring network coverage like the GAS.

Ecological distribution of the shrimp Nematopalaemon schmitti (Crustacea: Decapoda: Caridea) in three bays on the south-eastern coast of Brazil

The relationships between the spatial and temporal variations in the abundance of the shrimp Nematopalaemon schmitti and water temperature, salinity, and texture and organic-matter content of the sediment, were analysed in Ubatumirim, Ubatuba and Mar Virado bays on the northern coast of São Paulo, Brazil. Sampling was carried out monthly, from January 1998 through December 1999, from a shrimp boat equipped with double-rig nets, along six transects in each bay. In total, 2 116 specimens of N. schmitti were caught. Their distribution differed among bays, transects and seasons (ANOVA, p < 0.05). Highest total abundance was found in areas of high organic-matter content, in substrate composed mainly of very fine sand and silt and clay, and during winter and autumn. Although multiple regression analysis showed no significant relationship (p > 0.05), observations suggest that water temperature, sediment texture, organic-matter content, and the presence of biodetritus and plant fragments, provided favourable environmental conditions for the establishment of N. schmitti in the region.